Relevant bibliographies by topics / Hybrid programming models

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Hybrid programming models'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Hybrid programming models"

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Andrews, D., D. Niehaus, and P. Ashenden. "Programming models for hybrid CPU/FPGA chips." Computer 37, no. 1 (January 2004): 118–20. http://dx.doi.org/10.1109/mc.2004.1260732.

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Alghamdi, Ahmed Mohammed, Fathy Elbouraey Eassa, Maher Ali Khamakhem, Abdullah Saad AL-Malaise AL-Ghamdi, Ahmed S. Alfakeeh, Abdullah S. Alshahrani, and Ala A. Alarood. "Parallel Hybrid Testing Techniques for the Dual-Programming Models-Based Programs." Symmetry 12, no. 9 (September 20, 2020): 1555. http://dx.doi.org/10.3390/sym12091555.

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The importance of high-performance computing is increasing, and Exascale systems will be feasible in a few years. These systems can be achieved by enhancing the hardware’s ability as well as the parallelism in the application by integrating more than one programming model. One of the dual-programming model combinations is Message Passing Interface (MPI) + OpenACC, which has several features including increased system parallelism, support for different platforms with more performance, better productivity, and less programming effort. Several testing tools target parallel applications built by using programming models, but more effort is needed, especially for high-level Graphics Processing Unit (GPU)-related programming models. Owing to the integration of different programming models, errors will be more frequent and unpredictable. Testing techniques are required to detect these errors, especially runtime errors resulting from the integration of MPI and OpenACC; studying their behavior is also important, especially some OpenACC runtime errors that cannot be detected by any compiler. In this paper, we enhance the capabilities of ACC_TEST to test the programs built by using the dual-programming models MPI + OpenACC and detect their related errors. Our tool integrated both static and dynamic testing techniques to create ACC_TEST and allowed us to benefit from the advantages of both techniques reducing overheads, enhancing system execution time, and covering a wide range of errors. Finally, ACC_TEST is a parallel testing tool that creates testing threads based on the number of application threads for detecting runtime errors.
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Jamaluddin, Muhammad Na’im Fikri, Azlan Ismail, Amir Abd Rashid, and Talha Takleh Omar Takleh. "Performance comparison of java based parallel programming models." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (December 1, 2019): 1577. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1577-1583.

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<span lang="EN-MY">Parallel programming has been implemented in many areas to solve various computational problem with the aim, to improve the performance and scalability of the software application. There are a few parallel programming models commonly used, namely, threads, and message passing (distributed) models. Furthermore, various APIs have been proposed to implement these models based on two popular languages, notably, C/C++ and Java. A few studies have been done to compare the performance of parallel programming models, specifically, pure versus hybrid model. However, most of existing comparisons targeted on MPI/OpenMP based on C/C++ language. In this paper, our aim is to explore the performance comparison between threads, message passing and hybrid model in Java, specifically using Java multithreading and MPJ Express. For this reason, we have chosen a problem called word count occurrence which is significant in Natural Language Processing and use it to design and implement the parallel programs. We then present their performance and discuss the results.</span>
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Li, Dong, Bronis R. de Supinski, Martin Schulz, Dimitrios S. Nikolopoulos, and Kirk W. Cameron. "Strategies for Energy-Efficient Resource Management of Hybrid Programming Models." IEEE Transactions on Parallel and Distributed Systems 24, no. 1 (January 2013): 144–57. http://dx.doi.org/10.1109/tpds.2012.95.

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Wang, Zhi-Cheng, and Xiao-Bei Wu. "Hybrid Biogeography-Based Optimization for Integer Programming." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/672983.

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Biogeography-based optimization (BBO) is a relatively new bioinspired heuristic for global optimization based on the mathematical models of biogeography. By investigating the applicability and performance of BBO for integer programming, we find that the original BBO algorithm does not perform well on a set of benchmark integer programming problems. Thus we modify the mutation operator and/or the neighborhood structure of the algorithm, resulting in three new BBO-based methods, named BlendBBO, BBO_DE, and LBBO_LDE, respectively. Computational experiments show that these methods are competitive approaches to solve integer programming problems, and the LBBO_LDE shows the best performance on the benchmark problems.
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Andrews, D., D. Niehaus, R. Jidin, M. Finley, W. Peck, M. Frisbie, J. Ortiz, Ed Komp, and P. Ashenden. "Programming models for hybrid FPGA-cpu computational components: a missing link." IEEE Micro 24, no. 4 (July 2004): 42–53. http://dx.doi.org/10.1109/mm.2004.36.

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Rabenseifner, Rolf, and Gerhard Wellein. "Communication and Optimization Aspects of Parallel Programming Models on Hybrid Architectures." International Journal of High Performance Computing Applications 17, no. 1 (February 2003): 49–62. http://dx.doi.org/10.1177/1094342003017001005.

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Mohamed, Ahmed S. "Broader dynamic load balancing for hybrid/multi-level parallel programming models." International Journal of High Performance Computing and Networking 3, no. 2/3 (2005): 171. http://dx.doi.org/10.1504/ijhpcn.2005.008034.

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Sitek, Paweł, Krzysztof Bzdyra, and Jarosław Wikarek. "A Hybrid Method for Modeling and Solving Supply Chain Optimization Problems with Soft and Logical Constraints." Mathematical Problems in Engineering 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/1532420.

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This paper presents a hybrid method for modeling and solving supply chain optimization problems with soft, hard, and logical constraints. Ability to implement soft and logical constraints is a very important functionality for supply chain optimization models. Such constraints are particularly useful for modeling problems resulting from commercial agreements, contracts, competition, technology, safety, and environmental conditions. Two programming and solving environments, mathematical programming (MP) and constraint logic programming (CLP), were combined in the hybrid method. This integration, hybridization, and the adequate multidimensional transformation of the problem (as a presolving method) helped to substantially reduce the search space of combinatorial models for supply chain optimization problems. The operation research MP and declarative CLP, where constraints are modeled in different ways and different solving procedures are implemented, were linked together to use the strengths of both. This approach is particularly important for the decision and combinatorial optimization models with the objective function and constraints, there are many decision variables, and these are summed (common in manufacturing, supply chain management, project management, and logistic problems). TheECLiPSesystem with Eplex library was proposed to implement a hybrid method. Additionally, the proposed hybrid transformed model is compared with the MILP-Mixed Integer Linear Programming model on the same data instances. For illustrative models, its use allowed finding optimal solutions eight to one hundred times faster and reducing the size of the combinatorial problem to a significant extent.
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Chorley, Martin J., David W. Walker, and Martyn F. Guest. "Hybrid Message-Passing and Shared-Memory Programming in a Molecular Dynamics Application On Multicore Clusters." International Journal of High Performance Computing Applications 23, no. 3 (June 2, 2009): 196–211. http://dx.doi.org/10.1177/1094342009106188.

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Hybrid programming, whereby shared-memory and message-passing programming techniques are combined within a single parallel application, has often been discussed as a method for increasing code performance on clusters of symmetric multiprocessors (SMPs). This paper examines whether the hybrid model brings any performance benefits for clusters based on multicore processors. A molecular dynamics application has been parallelized using both MPI and hybrid MPI/OpenMP programming models. The performance of this application has been examined on two high-end multicore clusters using both Infiniband and Gigabit Ethernet interconnects. The hybrid model has been found to perform well on the higher-latency Gigabit Ethernet connection, but offers no performance benefit on low-latency Infiniband interconnects. The changes in performance are attributed to the differing communication profiles of the hybrid and MPI codes.
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Dissertations / Theses on the topic "Hybrid programming models"

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Wu, Zheng. "Hybrid Multi-Objective Optimization Models for Managing Pavement Assets." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/26092.

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Increasingly tighter budgets, changes in government role/function, declines in staff resources, and demands for increased accountability in the transportation field have brought unprecedented challenges for state transportation officials at all management levels. Systematic methodologies for effective management of a specific type of infrastructure (e.g., pavement and bridges) as well as for holistically managing all types of infrastructure assets are being developed to approach these challenges. In particular, the intrinsic characteristics of highway system make the use of multi-objective optimization techniques particularly attractive for managing highway assets. Recognizing the need for effective tradeoff tools and the limitations of state-of-practice analytical models and tools in highway asset management, the main objective of this dissertation was to develop a performance-based asset management framework that uses multi-objective optimization techniques and consists of stand-alone but logically interconnected optimization models for different management levels. Based on a critical review of popular multi-objective optimization techniques and their applications in highway asset management, a synergistic integration of complementary multi-criteria optimization techniques is recommended for the development of practical and efficient decision-supporting tools. Accordingly, the dissertation first proposes and implements a probabilistic multi-objective model for performance-based pavement preservation programming that uses the weighting sum method and chance constraints. This model can handle multiple incommensurable and conflicting objectives while considering probabilistic constraints related to the available budget over the planning horizon, but is found more suitable to problems with small number of objective functions due to its computational intensity. To enhance the above model, a hybrid model that requires less computing time and systematically captures the decision maker's preferences on multiple objectives is developed by combining the analytic hierarchy process and goal programming. This model is further extended to also capture the relative importance existent within optimization constraints to be suitable for allocations of funding across multiple districts for a decentralized state department of transportation. Finally, as a continuation of the above proposed models for the succeeding management level, a project selection model capable of incorporating qualitative factors (e.g. equity, user satisfaction) into the decision making is developed. This model combines k-means clustering, analytic hierarchy process and integer linear programming. All the models are logically interconnected in a comprehensive resource allocation framework. Their feasibility, practicality and potential benefits are illustrated through various case studies and recommendations for further developments are provided.
Ph. D.
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Cai, Xinye. "A multi-objective GP-PSO hybrid algorithm for gene regulatory network modeling." Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1492.

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Szczepanski, Nicolas. "SAT en Parallèle." Thesis, Artois, 2017. http://www.theses.fr/2017ARTO0403/document.

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La thèse porte sur la résolution des problèmes de satisfaisabilité booléenne (SAT) dans un cadre massivement parallèle. Le problème SAT est largement utilisé pour résoudre des problèmes combinatoires de première importance comme la vérification formelle de matériels et de logiciels, la bio-informatique, la cryptographie, la planification et l’ordonnancement de tâches. Plusieurs contributions sont apportées dans cette thèse. Elles vont de la conception d’algorithmes basés sur les approches « portfolio » et « diviser pour mieux régner », à l’adaptation de modèles de programmation parallèle, notamment hybride (destinés à des architectures à mémoire partagée et distribuée), à SAT, en passant par l’amélioration des stratégies de résolution. Ce travail de thèse a donné lieu à plusieurs contributions dans des conférences internationales du domaine ainsi qu’à plusieurs outils (open sources) de résolution des problèmes SAT, compétitifs au niveau international
This thesis deals with propositional satisfiability (SAT) in a massively parallel setting. The SAT problem is widely used for solving several combinatorial problems (e.g. formal verification of hardware and software, bioinformatics, cryptography, planning, scheduling, etc.). The first contribution of this thesis concerns the design of efficient algorithms based on the approaches « portfolio » and « divide and conquer ». Secondly, an adaptation of several parallel programming models including hybrid (parallel and distributed computing) to SAT is proposed. This work has led to several contributions to international conferences and highly competitive distributed SAT solvers
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PONTE, THIAGO COSTA. "LUACHARM: A HYBRID MODEL USING SCRIPTING LANGUAGES FOR PARALEL PROGRAMMING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=30276@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Nos últimos anos, as linguagens de script ganharam muita importância em diversas áreas da computação. Uma das áreas onde essas linguagens ainda são pouco exploradas é na área de computação paralela. A computação paralela sempre foi fortemente associada a computação científica, mas recentemente ela ganhou uma nova importância com a popularização de processadores multi-core. Com esse crescimento se torna necessário o surgimento de novos paradigmas de programação paralela para facilitar o desenvolvimento e dinamizar as aplicações, e linguagens de script podem ser usadas para isso, trazendo dinamismo, simplicidade e flexibilidade às aplicações. Esta dissertação visa estudar um modelo híbrido de programação entre duas linguagens de programação, Lua e Charm plus plus.
Recently, scripting languages have become very important in many fields of computer science. One area in which these languages have not been explored is paralel programming. Paralel programming has always been strongly associated with scientific usage, but recently, with the growth in popularity of multi core systems, it has gained a new field of action. With this change, the development of new programming paradigms of paralel programming become necessary in order to make development easier and applications more dynamic. Scripting languages may be used for this, bringing dynamics, flexibility and simplicity to aplications. This dissertation aims to study a hybrid programming model with two programming languages, Charm plus plus and Lua.
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Rivotti, Pedro. "Multi-parametric programming and explicit model predictive control of hybrid systems." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24432.

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This thesis is concerned with different topics in multi-parametric programming and explicit model predictive control, with particular emphasis on hybrid systems. The main goal is to extend the applicability of these concepts to a wider range of problems of practical interest, and to propose algorithmic solutions to challenging problems such as constrained dynamic programming of hybrid linear systems and nonlinear explicit model predictive control. The concepts of multi-parametric programming and explicit model predictive control are presented in detail, and it is shown how the solution to explicit model predictive control may be efficiently computed using a combination of multi-parametric programming and dynamic programming. A novel algorithm for constrained dynamic programming of mixed-integer linear problems is proposed and illustrated with a numerical example that arises in the context of inventory scheduling. Based on the developments on constrained dynamic programming of mixed-integer linear problems, an algorithm for explicit model predictive control of hybrid systems with linear cost function is presented. This method is further extended to the design of robust explicit controllers for hybrid linear systems for the case when uncertainty is present in the model. The final part of the thesis is concerned with developments in nonlinear explicit model predictive control. By using suitable model reduction techniques, the model captures the essential nonlinear dynamics of the system, while the achieved reduction in dimensionality allows the use of nonlinear multi-parametric programming methods.
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Ñahuis, Fernando Vladimir Cerna [UNESP]. "Modelos de programação matemática para o gerenciamento de energia em modernos sistemas de distribuição de energia elétrica." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/148871.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Nesta tese são apresentados três modelos de programação matemática que abordam os problemas de otimização relacionados ao gerenciamento da energia nos sistemas de distribuição de energia elétrica (SDEE), como: 1) Programação ótima das entregas e carregamento dos veículos elétricos (VEs) durante a navegação em um mapa de cidade, 2) Gerenciamento ótimo pelo lado da demanda considerando um sistema fotovoltaico híbrido (SFH) em uma residência em baixa tensão (RBT) no SDEE, e 3) O melhoramento do fator de carga (FC) do SDEE através do controle da demanda. O primeiro problema visa minimizar os custos relacionados com a manutenção e geração de horas extra durante a operação de uma frota de VEs, levando em conta um conjunto de entregas pre-especificadas, assim como, pontos de carregamento alocados ao longo de cada via urbana (principal e/ou secundária) pertencente ao mapa da cidade. No segundo problema, para uma residência em baixa tensão é planejado um perfil ótimo de consumo para o dia seguinte. Este perfil de consumo é obtido através de um programa de gerenciamento pelo lado da demanda (GLD) que considera uma estrutura tarifária e um esquema de operação que otimiza os recursos energéticos vindos de um SFH e o SDEE. Para cada problema de otimização é apresentado o seu correspondente modelo de programação não linear inteiro misto (PNLIM). O terceiro problema visa minimizar os custos por compra de energia (consumo e perdas de potência ativa) da concessionária, levando em conta, o controle da demanda dos usos-finais, presentes nas unidades consumidoras (residenciais, comerciais, e industriais) no SDEE. As incertezas na utilização dos usos-finais nas unidades consumidoras são simuladas através de um algoritmo Monte Carlo. Além disso, o modelo proposto PIMRQ é rodado dentro de um processo iterativo, que visa a melhoria do FC do SDEE. Por outro lado, através destes modelos não-lineares, a solução ótima global não é garantida, enquanto o uso de modelos equivalentes (para o primeiro e segundo problema, sendo um modelo aproximado para o terceiro) de programação linear inteira mista (PLIM) resolvidos por ferramentas de otimização clássica existentes garantem a convergência para a solução ótima global. Por conseguinte, para resolver este inconveniente, os seus modelos MILP equivalentes são obtidos e explicados em detalhe. Os modelos propostos foram implementados na linguagem de modelagem algébrica AMPL e resolvidos usando o solver comercial CPLEX. Além disso, algoritmos de simulação para representar as incertezas dos tempos de demora na operação dos VEs e os hábitos de utilização dos usos-finais durante o dia, são desenvolvidos. Um grafo unidirecional de 71 nós, uma rede elétrica IEEE de 34 nós, e 21 usos-finais (incluído um VE plug-in para o carregamento na residência) residenciais são utilizados para testar a precisão e a eficiência, assim como, também técnica de solução dos modelos propostos para cada problema.
This thesis presents three mathematical programming models to address the optimization problems related to the energy management in the electricity distribution systems (EDSs), such as: 1) Optimal delivery scheduling and charging of electric vehicles (EVs) in the navigation of a city map, 2) Optimal demand side management of an EDS considering a hybrid photovoltaic system (HPS) in a residential low voltage (RLV), and 3) Load factor improvement through the demand control in the EDS. The first problem aims at minimizing the costs related to the maintenance and generation of extra hours during the operation of a EVs fleet, taking into account a number of prespecified deliveries, as well as charging points allocated along each urban road (main or secondary) belongs to the city map. In the second problem, for a RLV, an optimal consumption profile of a day-ahead is planned. This consumption profile is obtained through a demand side management (DSM) program that considers a tariff structure and an operating scheme that optimizes the energy resources coming from HFS and EDS. The third problem aims at minimizing the costs of energy purchase (consumption and active energy losses) of the company, taking into account, the demand control of the end-uses, presents in the consumers units (residential, commercial, and industrial) in the EDS. Uncertainties in the use of the end-uses in the different consumer units are simulated through a Monte Carlo algorithm that determines a habitual consumption profile for EDSs. Based on this habitual profile, the proposed MIPRQ model determines an optimal profile for EDSs. This model uses an iterative process that aims to improve the load factor of the EDS. For each optimization problem the corresponding non-linear mixed integer programming (NLMIP) model is presented. On the other hand, via these nonlinear models, the global optimal solution is not guaranteed, while using the equivalent mixed-integer linear (MILP) models (for the first and second problems, being an approximate model for the third) and solving them by existing classical optimization tools ensures convergence to global optimal solution. Therefore, in order to address this drawback, their equivalent mixed integer linear programming (MILP) models are obtained and explained in detail. The proposed models are implemented in the algebraic modeling language AMPL and solved using the commercial CPLEX solver. Moreover, simulations algorithms to represent the uncertainties of delay times in the operation of EVs and usage habits of end-uses during the day, are developed. A multidirectional graph with 71 nodes, an electrical network IEEE 34 nodes, and a quantity of 21 residential end-uses (including an EV plug-in for residential charging) are used to test the precision and the efficiency, as well as the solution technique of the models proposed for each problem.
CNPq: 141462/2013- 2
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Ñahuis, Fernando Vladimir Cerna. "Modelos de programação matemática para o gerenciamento de energia em modernos sistemas de distribuição de energia elétrica /." Ilha Solteira, 2017. http://hdl.handle.net/11449/148871.

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Orientador: Marcos Júlio Rider Flores
Resumo: Nesta tese são apresentados três modelos de programação matemática que abordam os problemas de otimização relacionados ao gerenciamento da energia nos sistemas de distribuição de energia elétrica (SDEE), como: 1) Programação ótima das entregas e carregamento dos veículos elétricos (VEs) durante a navegação em um mapa de cidade, 2) Gerenciamento ótimo pelo lado da demanda considerando um sistema fotovoltaico híbrido (SFH) em uma residência em baixa tensão (RBT) no SDEE, e 3) O melhoramento do fator de carga (FC) do SDEE através do controle da demanda. O primeiro problema visa minimizar os custos relacionados com a manutenção e geração de horas extra durante a operação de uma frota de VEs, levando em conta um conjunto de entregas pre-especificadas, assim como, pontos de carregamento alocados ao longo de cada via urbana (principal e/ou secundária) pertencente ao mapa da cidade. No segundo problema, para uma residência em baixa tensão é planejado um perfil ótimo de consumo para o dia seguinte. Este perfil de consumo é obtido através de um programa de gerenciamento pelo lado da demanda (GLD) que considera uma estrutura tarifária e um esquema de operação que otimiza os recursos energéticos vindos de um SFH e o SDEE. Para cada problema de otimização é apresentado o seu correspondente modelo de programação não linear inteiro misto (PNLIM). O terceiro problema visa minimizar os custos por compra de energia (consumo e perdas de potência ativa) da concessionária, levando em conta, o co... (Resumo completo, clicar acesso eletrônico abaixo)
Doutor
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Bhikadiya, Ruchit Anilbhai. "Hybrid Vehicle Control Benchmark." Thesis, Linköpings universitet, Fordonssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171586.

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The new emission regulations for new trucks was made to decrease the CO2 emissions by 30% from 2020 to 2030. One of the solutions is hybridizing the truck powertrain with 48V or 600V that can recover brake energy with electrical machines and batteries. The control of this hybrid powertrain is key to increase fuel efficiency. The idea behind this approach is to combine two different power sources, an internal combustion engine and a battery driven electric machine, and use both to provide tractive forces to the vehicle. This approach requires a HEV controller to operate the power flow within the systems. The HEV controller is the key to maximize fuel savings which contains an energy management strategy. It uses the knowledge of the road profile ahead by GPS and maps, and strongly interacts with the control of the cruise speed, automated gear shifts, powertrain modes and state of charge. In this master thesis, the dynamic programming strategy is used as predictive energy management for hybrid electric truck in forward- facing simulation environment. An analysis of predictive energy management is thus done for receding and full horizon length on flat and hilly drive cycle, where fuel consumption and recuperation energy will be regarded as the primary factor. Another important factor to consider is the powertrain mode of the vehicle with different penalty values. The result from horizon study indicates that the long receding horizon length has a benefit to store more recuperative energy. The fuel consumption is decreased for all drive cycle in the comparison with existing Volvo’s strategy.
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Axehill, Daniel. "Integer Quadratic Programming for Control and Communication." Doctoral thesis, Linköpings universitet, Reglerteknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10642.

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The main topic of this thesis is integer quadratic programming with applications to problems arising in the areas of automatic control and communication. One of the most widespread modern control methods is Model Predictive Control (MPC). In each sampling time, MPC requires the solution of a Quadratic Programming (QP) problem. To be able to use MPC for large systems, and at high sampling rates, optimization routines tailored for MPC are used. In recent years, the range of application of MPC has been extended to so-called hybrid systems. Hybrid systems are systems where continuous dynamics interact with logic. When this extension is made, binary variables are introduced in the problem. As a consequence, the QP problem has to be replaced by a far more challenging Mixed Integer Quadratic Programming (MIQP) problem, which is known to have a computational complexity which grows exponentially in the number of binary optimization variables. In modern communication systems, multiple users share a so-called multi-access channel. To estimate the information originally sent, a maximum likelihood problem involving binary variables can be solved. The process of simultaneously estimating the information sent by multiple users is called Multiuser Detection (MUD). In this thesis, the problem to efficiently solve MIQP problems originating from MPC and MUD is addressed. Four different algorithms are presented. First, a polynomial complexity preprocessing algorithm for binary quadratic programming problems is presented. By using the algorithm, some, or all, binary variables can be computed efficiently already in the preprocessing phase. In numerical experiments, the algorithm is applied to unconstrained MPC problems with a mixture of real valued and binary valued control signals, and the result shows that the performance gain can be significant compared to solving the problem using branch and bound. The preprocessing algorithm has also been applied to the MUD problem, where simulations have shown that the bit error rate can be significantly reduced compared to using common suboptimal algorithms. Second, an MIQP algorithm tailored for MPC is presented. The algorithm uses a branch and bound method where the relaxed node problems are solved by a dual active set QP algorithm. In this QP algorithm, the KKT systems are solved using Riccati recursions in order to decrease the computational complexity. Simulation results show that both the proposed QP solver and MIQP solver have lower computational complexity compared to corresponding generic solvers. Third, the dual active set QP algorithm is enhanced using ideas from gradient projection methods. The performance of this enhanced algorithm is shown to be comparable with the existing commercial state-of-the-art QP solver \cplex for some random linear MPC problems. Fourth, an algorithm for efficient computation of the search directions in an SDP solver for a proposed alternative SDP relaxation applicable to MPC problems with binary control signals is presented. The SDP relaxation considered has the potential to give a tighter lower bound on the optimal objective function value compared to the QP relaxation that is traditionally used in branch and bound for these problems, and its computational performance is better than the ordinary SDP relaxation for the problem. Furthermore, the tightness of the different relaxations is investigated both theoretically and in numerical experiments.
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Engman, Jimmy. "Model Predictive Control for Series-Parallel Plug-In Hybrid Electrical Vehicle." Thesis, Linköpings universitet, Fordonssystem, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-69608.

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The automotive industry is required to deal with increasingly stringent legislationfor greenhouse gases. Hybrid Electric Vehicles, HEV, are gaining acceptance as thefuture path of lower emissions and fuel consumption. The increased complexityof multiple prime movers demand more advanced control systems, where futuredriving conditions also becomes interesting. For a plug-in Hybrid Electric Vehicle,PIHEV, it is important to utilize the comparatively inexpensive electric energybefore the driving cycle is complete, this for minimize the cost of the driving cycle,since the battery in a PIHEV can be charged from the grid. A strategy with lengthinformation of the driving cycle from a global positioning system, GPS, couldreduce the cost of driving. This by starting to blend the electric energy with fuelearlier, a strategy called blended driving accomplish this by distribute the electricenergy, that is charged externally, with fuel over the driving cycle, and also ensurethat the battery’s minimum level reaches before the driving cycle is finished. Astrategy called Charge Depleting Charge Sustaining, CDCS, does not need lengthinformation. This strategy first depletes the battery to a minimum State of Charge,SOC, and after this engages the engine to maintain the SOC at this level. In thisthesis, a variable SOC reference is developed, which is dependent on knowledgeabout the cycle’s length and the current length the vehicle has driven in the cycle.With assistance of a variable SOC reference, is a blended strategy realized. Thisis used to minimize the cost of a driving cycle. A comparison between the blendedstrategy and the CDCS strategy was done, where the CDCS strategy uses a fixedSOC reference. During simulation is the usage of fuel minimized; and the blendedstrategy decreases the cost of the driving missions compared to the CDCS strategy.To solve the energy management problem is a model predictive control used. Thedesigned control system follows the driving cycles, is charge sustaining and solvesthe energy management problem during simulation. The system also handlesmoderate model errors.
Fordonsindustrin måste hantera allt strängare lagkrav mot utsläpp av emissioneroch växthusgaser. Hybridfordon har börjat betraktas som den framtida vägenför att ytterligare minska utsläpp och användning av fossila bränslen. Den ökadekomplexiteten från flera olika motorer kräver mera avancerade styrsystem. Begränsningarfrån motorernas energikällor gör att framtida förhållanden är viktigaatt estimera. För plug-in hybridfordon, PIHEV, är det viktigt att använda denvvijämförelsevis billiga elektriska energin innan fordonet har nått fram till slutdestinationen.Batteriets nuvarande energimängd mäts i dess State of Charge, SOC.Genom att utnyttja information om hur långt det är till slutdestinationen från ettGlobal Positioning System, GPS, blandar styrsystemet den elektriska energin medbränsle från början, detta kallas för blandad körning. En strategi som inte hartillgång till hur långt fordonet ska köras kallas Charge Depleting Charge Sustaining,CDCS. Denna strategi använder först energin från batteriet, för att sedanbörja använda förbränningsmotorn när SOC:s miniminivå har nåtts. Strategin attanvända GPS informationen är jämförd med en strategi som inte har tillgång tillinformation om körcykelns längd. Blandad körning använder en variabel SOC referens,till skillnad från CDCS strategin som använder sig av en konstant referenspå SOC:s miniminivå. Den variabla SOC referensen beror på hur långt fordonethar kört av den totala körsträckan, med hjälp av denna realiseras en blandad körning.Från simuleringarna visade det sig att blandad körning gav minskad kostnadför de simulerade körcyklerna jämfört med en CDCS strategi. En modellbaseradprediktionsreglering används för att lösa energifördelningsproblemet. Styrsystemetföljer körcykler och löser energifördelningsproblemet för de olika drivkällorna undersimuleringarna. Styrsystemet hanterar även måttliga modellfel.
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Books on the topic "Hybrid programming models"

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Bäck, Thomas. Evolutionary Algorithms in Theory and Practice. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099713.001.0001.

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This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixed integer optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields.
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Book chapters on the topic "Hybrid programming models"

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Mutlu, Erdal, Vladimir Gajinov, Adrián Cristal, Serdar Tasiran, and Osman S. Unsal. "Dynamic Verification for Hybrid Concurrent Programming Models." In Runtime Verification, 156–61. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11164-3_13.

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Hoefler, Torsten, Greg Bronevetsky, Brian Barrett, Bronis R. de Supinski, and Andrew Lumsdaine. "Efficient MPI Support for Advanced Hybrid Programming Models." In Recent Advances in the Message Passing Interface, 50–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15646-5_6.

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Gropp, William D. "MPI and Hybrid Programming Models for Petascale Computing." In Recent Advances in Parallel Virtual Machine and Message Passing Interface, 6–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87475-1_5.

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Tsugane, Keisuke, Taisuke Boku, Hitoshi Murai, Mitsuhisa Sato, William Tang, and Bei Wang. "Hybrid-View Programming of Nuclear Fusion Simulation Code in XcalableMP." In XcalableMP PGAS Programming Language, 181–203. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7683-6_7.

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AbstractXcalableMP(XMP) supports a global-view model that allows programmers to define global data and to map them to a set of processors, which execute the distributed global data as a single thread. In XMP, the concept of a coarray is also employed for local-view programming. In this study, we port Gyrokinetic Toroidal Code - Princeton (GTC-P), which is a three-dimensional gyrokinetic PIC code developed at Princeton University to study the microturbulence phenomenon in magnetically confined fusion plasmas, to XMP as an example of hybrid memory model coding with the global-view and local-view programming models. In local-view programming, the coarray notation is simple and intuitive compared with Message Passing Interface (MPI) programming, while the performance is comparable to that of the MPI version. Thus, because the global-view programming model is suitable for expressing the data parallelism for a field of grid space data, we implement a hybrid-view version using a global-view programming model to compute the field and a local-view programming model to compute the movement of particles. The performance is degraded by 20% compared with the original MPI version, but the hybrid-view version facilitates more natural data expression for static grid space data (in the global-view model) and dynamic particle data (in the local-view model), and it also increases the readability of the code for higher productivity.
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Rabenseifner, Rolf. "Communication Bandwidth of Parallel Programming Models on Hybrid Architectures." In Lecture Notes in Computer Science, 401–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47847-7_37.

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Refalo, Philippe. "Linear Formulation of Constraint Programming Models and Hybrid Solvers." In Principles and Practice of Constraint Programming – CP 2000, 369–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45349-0_27.

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Artigues, Christian, Emmanuel Hebrard, Valentin Mayer-Eichberger, Mohamed Siala, and Toby Walsh. "SAT and Hybrid Models of the Car Sequencing Problem." In Integration of AI and OR Techniques in Constraint Programming, 268–83. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07046-9_19.

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Laurel, Jacob, and Sasa Misailovic. "Continualization of Probabilistic Programs With Correction." In Programming Languages and Systems, 366–93. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44914-8_14.

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AbstractProbabilistic Programming offers a concise way to represent stochastic models and perform automated statistical inference. However, many real-world models have discrete or hybrid discrete-continuous distributions, for which existing tools may suffer non-trivial limitations. Inference and parameter estimation can be exceedingly slow for these models because many inference algorithms compute results faster (or exclusively) when the distributions being inferred are continuous. To address this discrepancy, this paper presents Leios. Leios is the first approach for systematically approximating arbitrary probabilistic programs that have discrete, or hybrid discrete-continuous random variables. The approximate programs have all their variables fully continualized. We show that once we have the fully continuous approximate program, we can perform inference and parameter estimation faster by exploiting the existing support that many languages offer for continuous distributions. Furthermore, we show that the estimates obtained when performing inference and parameter estimation on the continuous approximation are still comparably close to both the true parameter values and the estimates obtained when performing inference on the original model.
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Yaakob, Shamshul Bahar, and Junzo Watada. "A Hybrid Intelligent Algorithm for Solving the Bilevel Programming Models." In Knowledge-Based and Intelligent Information and Engineering Systems, 485–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15390-7_50.

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Jose, Jithin, Sreeram Potluri, Karen Tomko, and Dhabaleswar K. Panda. "Designing Scalable Graph500 Benchmark with Hybrid MPI+OpenSHMEM Programming Models." In Lecture Notes in Computer Science, 109–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38750-0_9.

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Conference papers on the topic "Hybrid programming models"

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Mohanty, Siddharth, and Murray Cole. "Autotuning Wavefront Applications for Multicore Multi-GPU Hybrid Architectures." In Programming Models and Applications. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/2578948.2560689.

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Jain, Surabhi, Gengbin Zheng, Maria Garzaran, James H. Cownie, Taru Doodi, and Terry L. Wilmarth. "Parallelizing MPI Using Tasks for Hybrid Programming Models." In 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2018. http://dx.doi.org/10.1109/ipdpsw.2018.00203.

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Grosan, C., and A. Abraham. "Ensemble of genetic programming models for designing reactive power controllers." In Fifth International Conference on Hybrid Intelligent Systems (HIS'05). IEEE, 2005. http://dx.doi.org/10.1109/ichis.2005.36.

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Pope, Bernard J., Blake G. Fitch, Michael C. Pitman, John J. Rice, and Matthias Reumann. "Petascale computation performance of lightweight multiscale cardiac models using hybrid programming models." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6090058.

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Dureau, David, and Gaël Poëtte. "Hybrid Parallel Programming Models for AMR Neutron Monte-Carlo Transport." In SNA + MC 2013 - Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo, edited by D. Caruge, C. Calvin, C. M. Diop, F. Malvagi, and J. C. Trama. Les Ulis, France: EDP Sciences, 2014. http://dx.doi.org/10.1051/snamc/201404202.

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Hashmi, Jahanzeb Maqbool, Khaled Hamidouche, and Dhabaleswar K. Panda. "Enabling Performance Efficient Runtime Support for Hybrid MPI+UPC++ Programming Models." In 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 2016. http://dx.doi.org/10.1109/hpcc-smartcity-dss.2016.0165.

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Jose, Jithin, Sreeram Potluri, Hari Subramoni, Xiaoyi Lu, Khaled Hamidouche, Karl Schulz, Hari Sundar, and Dhabaleswar K. Panda. "Designing Scalable Out-of-core Sorting with Hybrid MPI+PGAS Programming Models." In the 8th International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2676870.2676880.

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Gajinov, Vladimir, Srđan Stipić, Igor Erić, Osman S. Unsal, Eduard Ayguadé, and Adrián Cristal. "DaSH: a benchmark suite for hybrid dataflow and shared memory programming models." In CF'14: Computing Frontiers Conference. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2597917.2597942.

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Say, Buser, Ga Wu, Yu Qing Zhou, and Scott Sanner. "Nonlinear Hybrid Planning with Deep Net Learned Transition Models and Mixed-Integer Linear Programming." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/104.

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In many real-world hybrid (mixed discrete continuous) planning problems such as Reservoir Control, Heating, Ventilation and Air Conditioning (HVAC), and Navigation, it is difficult to obtain a model of the complex nonlinear dynamics that govern state evolution. However, the ubiquity of modern sensors allow us to collect large quantities of data from each of these complex systems and build accurate, nonlinear deep network models of their state transitions. But there remains one major problem for the task of control -- how can we plan with deep network learned transition models without resorting to Monte Carlo Tree Search and other black-box transition model techniques that ignore model structure and do not easily extend to mixed discrete and continuous domains? In this paper, we make the critical observation that the popular Rectified Linear Unit (ReLU) transfer function for deep networks not only allows accurate nonlinear deep net model learning, but also permits a direct compilation of the deep network transition model to a Mixed-Integer Linear Program (MILP) encoding in a planner we call Hybrid Deep MILP Planning (HD-MILP-PLAN). We identify deep net specific optimizations and a simple sparsification method for HD-MILP-PLAN that improve performance over a naive encoding, and show that we are able to plan optimally with respect to the learned deep network.
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Nakajima, Kengo. "New strategy for coarse grid solvers in parallel multigrid methods using OpenMP/MPI hybrid programming models." In the 2012 International Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2141702.2141713.

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